[pre-commit.ci] pre-commit suggestions (#895)

Co-authored-by: otaj <[email protected]>

.pre-commit-config.yaml

@@ -22,20 +22,20 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.34.0
+    rev: v2.38.2
     hooks:
       - id: pyupgrade
         args: [--py36-plus]
         name: Upgrade code
 
-  - repo: https://github.com/myint/docformatter
-    rev: v1.4
+  - repo: https://github.com/PyCQA/docformatter
+    rev: v1.5.0
     hooks:
       - id: docformatter
         args: [--in-place, --wrap-summaries=115, --wrap-descriptions=120]
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.14
+    rev: 0.7.16
     hooks:
       - id: mdformat
         additional_dependencies:
@@ -50,17 +50,17 @@ repos:
       - id: isort
 
   - repo: https://github.com/psf/black
-    rev: 22.6.0
+    rev: 22.8.0
     hooks:
       - id: black
         name: Format code
 
   - repo: https://github.com/asottile/yesqa
-    rev: v1.3.0
+    rev: v1.4.0
     hooks:
       - id: yesqa
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 4.0.1
+    rev: 5.0.4
     hooks:
       - id: flake8

pl_bolts/models/self_supervised/resnets.py

@@ -285,10 +285,12 @@ def _resnet(arch, block, layers, pretrained, progress, **kwargs):
     return model
 
 
+RESNET_PAPER = '`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_'
+
+
 @under_review()
 def resnet18(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""ResNet-18 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    f"""ResNet-18 model from {RESNET_PAPER}
 
     Args:
         pretrained: If True, returns a model pre-trained on ImageNet
@@ -299,8 +301,7 @@ def resnet18(pretrained: bool = False, progress: bool = True, **kwargs):
 
 @under_review()
 def resnet34(pretrained=False, progress=True, **kwargs):
-    r"""ResNet-34 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    f"""ResNet-34 model from {RESNET_PAPER}
 
     Args:
         pretrained: If True, returns a model pre-trained on ImageNet
@@ -311,8 +312,7 @@ def resnet34(pretrained=False, progress=True, **kwargs):
 
 @under_review()
 def resnet50(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""ResNet-50 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    f"""ResNet-50 model from {RESNET_PAPER}
 
     Args:
         pretrained: If True, returns a model pre-trained on ImageNet
@@ -323,8 +323,7 @@ def resnet50(pretrained: bool = False, progress: bool = True, **kwargs):
 
 @under_review()
 def resnet101(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""ResNet-101 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    f"""ResNet-101 model from {RESNET_PAPER}
 
     Args:
         pretrained: If True, returns a model pre-trained on ImageNet
@@ -335,8 +334,7 @@ def resnet101(pretrained: bool = False, progress: bool = True, **kwargs):
 
 @under_review()
 def resnet152(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""ResNet-152 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    f"""ResNet-34 model from {RESNET_PAPER}
 
     Args:
         pretrained: If True, returns a model pre-trained on ImageNet
@@ -345,10 +343,14 @@ def resnet152(pretrained: bool = False, progress: bool = True, **kwargs):
     return _resnet("resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs)
 
 
+AGGREGATED_RESNET_PAPER = (
+    '`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_'
+)
+
+
 @under_review()
 def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""ResNeXt-50 32x4d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+    f"""ResNeXt-50 32x4d model from {AGGREGATED_RESNET_PAPER}
 
     Args:
         pretrained: If True, returns a model pre-trained on ImageNet
@@ -361,8 +363,7 @@ def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs):
 
 @under_review()
 def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""ResNeXt-101 32x8d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+    f"""ResNeXt-101 32x8d model from {AGGREGATED_RESNET_PAPER}
 
     Args:
         pretrained: If True, returns a model pre-trained on ImageNet
@@ -375,11 +376,9 @@ def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs):
 
 @under_review()
 def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""Wide ResNet-50-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is
+    the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The
+    number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
     channels, and in Wide ResNet-50-2 has 2048-1024-2048.
 
     Args:
@@ -392,11 +391,9 @@ def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs):
 
 @under_review()
 def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs):
-    r"""Wide ResNet-101-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
-    The model is the same as ResNet except for the bottleneck number of channels
-    which is twice larger in every block. The number of channels in outer 1x1
-    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is
+    the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The
+    number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
     channels, and in Wide ResNet-50-2 has 2048-1024-2048.
 
     Args:

pl_bolts/utils/arguments.py

@@ -78,7 +78,6 @@ def parse_lit_args(self, *args: Any, **kwargs: Any) -> Namespace:
 
 @under_review()
 def gather_lit_args(cls: Any, root_cls: Optional[Any] = None) -> List[LitArg]:
-
     if root_cls is None:
         if issubclass(cls, LightningModule):
             root_cls = LightningModule

tests/datamodules/test_datamodules.py

@@ -20,7 +20,6 @@
 
 
 def test_dev_datasets(datadir):
-
     ds = CIFAR10(data_dir=datadir)
     for _ in ds:
         pass
@@ -48,7 +47,6 @@ def _create_synth_Cityscapes_dataset(path_dir):
 
 
 def test_cityscapes_datamodule(datadir):
-
     _create_synth_Cityscapes_dataset(datadir)
 
     batch_size = 1

tests/datasets/test_datasets.py

@@ -17,7 +17,6 @@
 
 @pytest.mark.parametrize("batch_size,num_samples", [(16, 100), (1, 0)])
 def test_dummy_ds(catch_warnings, batch_size, num_samples):
-
     if num_samples > 0:
 
         ds = DummyDataset((1, 28, 28), (1,), num_samples=num_samples)
@@ -41,7 +40,6 @@ def test_dummy_ds(catch_warnings, batch_size, num_samples):
 
 @pytest.mark.parametrize("batch_size,size,num_samples", [(16, 32, 100), (1, 0, 0)])
 def test_rand_dict_ds(catch_warnings, batch_size, size, num_samples):
-
     if num_samples > 0 or size > 0:
         ds = RandomDictDataset(size, num_samples=num_samples)
         dl = DataLoader(ds, batch_size=batch_size)
@@ -83,7 +81,6 @@ def test_rand_ds(catch_warnings, batch_size, size, num_samples):
 
 @pytest.mark.parametrize("batch_size,size,num_samples", [(16, 32, 100), (1, 0, 0)])
 def test_rand_str_dict_ds(catch_warnings, batch_size, size, num_samples):
-
     if num_samples > 0 and size > 0:
         ds = RandomDictStringDataset(size=size, num_samples=100)
         dl = DataLoader(ds, batch_size=batch_size)